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• W4312589025 abstract "As an essential water quality parameter in aquaculture ponds, dissolved oxygen (DO) affects the growth and development of aquatic animals and their feeding and absorption. However, DO is easily influenced by external factors. It is not easy to make scientific and accurate predictions of DO concentration trends, especially in long-term predictions. This paper uses a one-dimensional convolutional neural network to extract the features of multidimensional input data. Bidirectional long and short-term memory neural network propagated forward and backward twice and thoroughly mined the before and after attribute relationship of each data of dissolved oxygen sequence. The attention mechanism focuses the model on the time series prediction step to improve long-term prediction accuracy. Finally, we built an integrated prediction model based on convolutional neural network (CNN), bidirectional long and short-term memory neural network (BiLSTM) and attention mechanism (AM), which is called CNN-BiLSTM-AM model. To determine the accuracy of the CNN-BiLSTM-AM model, we conducted short-term (30 minutes, one hour) and long-term (6 hours, 12 hours) experimental validation on real datasets monitored at two aquaculture farms in Yantai City, Shandong Province, China. Meanwhile, the performance was compared and visualized with support vector regression, recurrent neural network, long short-term memory neural network, CNN-LSTM model and CNN-BiLSTM model. The results show that compared with other comparative models, the proposed CNN-BiLSTM-AM model has an excellent performance in mean absolute error, root means square error, mean absolute percentage error and determination coefficient." @default.
• W4312589025 created "2023-01-05" @default.
• W4312589025 creator A5000198200 @default.
• W4312589025 creator A5056934032 @default.
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• W4312589025 date "2022-01-01" @default.
• W4312589025 modified "2023-09-27" @default.
• W4312589025 title "Prediction of dissolved oxygen concentration in aquaculture based on attention mechanism and combined neural network" @default.
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• W4312589025 doi "https://doi.org/10.3934/mbe.2023046" @default.
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